Electric grid operators are responsible for ensuring that electric supply and demand is always in balance. To meet this responsibility, an interconnected grid is divided into a number of control areas (CA). Electric grid operators monitor any mismatch between generation and load within their responsible control area(s) and continuously adjust supply (generation) to minimize the instantaneous mismatch and to ensure that over time it is near zero. Control areas are clearly defined regions of network topology within which the balancing of generation and load is controlled to ensure system frequency remains within limits. Historically, control areas generally included the assets of individual vertically integrated utilities, but today groups of utility control areas have been integrated into larger footprints and operated at a higher level by Regional Transmission Operators (RTOs). Operators perform Day-Ahead, Current-Day and Hour-Ahead forecasts. See the following websites and screen captures for examples. Referring to FIG. 1, as can be seen in the screen capture taken from the Electric Reliability Council of Texas (ERCOT)'s web site (FIG. 1 is a graph of ERCOT's Load Forecast vs Actual for Jan. 5, 2014), the difference between the actual System Load and the Current-Day Forecast can be as much as 5% (2,500 MW/hour shortage between 19:00 and 24:00 in this example leading to over a 12,500 MW shortage). ERCOT, for example, manages the flow of electric power to 23 million Texas customers. The above estimation error averages to (2,500 MW/23 million customers)=˜108 W per customer/per hour for the five hour time period.
Electric grid operators have been deploying Advanced Metering Infrastructure (AMI). The AMI system functions by utilizing a digital meter at customer location that collects, stores and sends near “real time” energy usage data through a communication network connected to a central computer system located at the electric utility. For example, FIGS. 2 and 3 illustrate graphs of household usages based on the AMI. The grid control area operators perform day-ahead, current-day, hour-ahead forecasts but they do not have the capability to represent real-time system operating conditions. Conventionally, the forecasts are based on historical and current system loads and temperatures. Weather data from NOAA National Weather Service stations can be used in the energy use forecast. For the end consumer, “smart” mechanisms to reduce energy consumption are being offered. One such example is the consumer thermostat offered by Nest (www.nest.com). The Nest thermostat utilizes algorithms to automatically set efficient temperatures based on detecting user preferences. While this product benefits the end consumer in lowering their energy bills, it (and other “smart” devices) can provide very accurate data to aid in future power estimates; however such devices are consumer focused.
While electric grid operator forecasting is accurate over time, day-ahead, current-day and in some cases hour-ahead demand can have variation, such as the current forecast differs actual by as much as 5% (in the range of 2500 MW). The shortcoming in electric grid operator forecasting is there is no way of predicting their customer's abnormal usage pattern.
In another area, network operators and service providers are collecting bandwidth usage data to support their data allowance metering (for example, AT&T DSL service 150 G per month, AT&T Uverse service 250 G per month, etc.). When the data allowance is exceeded, AT&T charges $10 per 50 G of data. For example, FIG. 4 illustrates a data trend graph and data usage table for a typical customer. It would be advantageous to leverage the data collected in real-time or near real-time by the network operators and service providers to enhance the electric grid operator forecasting and to particularly address predicting abnormal usage patterns.